Pham, D. L. | bioRxiv Science

bioRxiv2021

In Vivo MRI of Endogenous Remyelination in a Nonhuman Primate Model of Multiple Sclerosis

Remyelination is crucial for recovery from inflammatory demyelination in multiple sclerosis (MS). Investigating remyelination in vivo using magnetic resonance imaging (MRI) is difficult in MS, where collecting serial short-interval scans is challenging. Using experimental autoimmune encephalomyelitis (EAE) in common marmosets, a model of MS that recapitulates focal cerebral MS lesions, we investigated whether remyelination can be detected and characterized noninvasively. In 6 animals followed with multisequence 7-tesla MRI, 36 focal lesions, classified as demyelinated or remyelinated based on signal intensity on proton density-weighted images, were subsequently assessed with histopathology. Remyelination occurred in 5 of 6 marmosets and 51% of lesions. Radiological-pathological comparison showed high sensitivity (88%) and specificity (90%) for detecting remyelination by in vivo MRI. This study demonstrates the prevalence of spontaneous remyelination in marmoset EAE and the ability of in vivo MRI to detect it, with implications for preclinical testing of pro-remyelinating agents and translation to clinical practice.

neuroscience↗

bioRxiv2021

Validation of Data Acquisition and Phase Estimation for Quantitative Susceptibility Mapping with a Rotating-Tube Phantom

PurposeQuantitative Susceptibility Mapping (QSM) is an MRI tool with the potential to reveal pathological changes from magnetic susceptibility measurements. Before phase data can be used to recover susceptibility ({Delta}{chi}), the QSM process begins with two steps: data acquisition and phase estimation. We assess the performance of these steps, when applied without user intervention, on several variations of a phantom imaging task. ApproachWe used a rotating-tube phantom with five tubes ranging from {Delta}{chi}=0.05 ppm to {Delta}{chi}=0.336 ppm. MRI data was acquired at nine angles of rotation for four different pulse sequences. The images were processed by 10 phase estimation algorithms including Laplacian, region-growing, branch-cut, temporal unwrapping and maximum-likelihood methods. We analyzed errors between measured and expected phase using the probability mass function and Cumulative Distribution Function. ResultsRepeatable acquisition and estimation methods were identified based on the probability of relative phase errors. For single-echo GRE and segmented EPI sequences, a region-growing method was most reliable with Pr(relative error<0.1)=0.95 and 0.90 respectively. For multi-echo sequences, a Maximum-Likelihood method was most reliable with Pr(relative error<0.1)=0.97. The most repeatable multi-echo methods outperformed the most repeatable single-echo methods. ConclusionsWe found a wide range of repeatability and reproducibility for off-the-shelf MRI acquisition and phase estimation approaches. The error was dominated in many cases by spatially discontinuous phase unwrapping errors. Any post-processing applied on erroneous phase estimates, such as QSMs background field removal and dipole inversion, would suffer from error propagation. Our paradigm identifies methods that yield consistent and accurate phase estimates that would ultimately yield consistent and accurate {Delta}{chi} estimates.

bioengineering↗